Despite the dual objectives of many health care systems of improving total health and reducing health inequality
, trial designs seem to ignore the assessment of inequality
effects. Our study aimed to illustrate an empirical framework for the assessment of inequality
effects alongside policy-oriented trials to inform a possible efficiency versus equality trade-off.
We measured inequality
in the concentrations of all-cause and disease-related mortality and hospital admissions across ranks of socioeconomic status in a randomized controlled trial that tested the efficacy of general population screening
of men for vascular disease. We used alternative definitions of inequality
(relative/absolute, in attainment/shortfall, ranked by education/income), and supplemented the classical “frequentist” approach to statistical inference with Bayesian posterior probabilities. Equality contours for health improvement that leave inequality
unaffected are illustrated graphically. We used bootstrapping for interpretation.
We estimated the posterior probability of screening
to be between 0.21 and 0.93 depending on the inequality
definition. Income-ranked inequality
appeared to be generally higher than education-ranked inequality
but less affected by screening
. For the shortfall-relative index based on education-rank, the mean health improvement of a 7% relative reduction in all-cause mortality generated by screening
incurred a mean relative increase in inequality
of 28%. For the income-based indices, there was no evidence of a trade-off.
We illustrated how decision uncertainty can be reduced by explicit assessment of inequality
alongside trials and found some evidence of a possible equity–efficiency trade-off in the context of screening
, although this depended on the definition of equality.